BrainMap

Bogdan Strimbu

- OREGON STATE UNIVERSITY

Researcher | Teaching staff | PhD supervisor

Web of Science ResearcherID: not public

Expertise & keywords

Modeling stand and landscape processes using unmanned aerial systems Call name:
OREZ-FERM-875 2016 - 2021

Role in this project: Project coordinator

Coordinating institution: Oregon State University

Project partners: Oregon State University ()

Affiliation: Oregon State University ()

Project website:

Abstract:

The aim of the project is threefold: 1) identification of optimal inventory techniques and designs using UAS, 2) estimation of forest resources and 3) modeling of forest processes from fused data supplied by UAS, satellites, and airplanes. To achieve the goals the project will focus on five objectives:

• Optimize forest inventory using a combination of low-cost flying platforms and accurate tree segmentation algorithms

• Develop a method of forest inventory using “under the canopy moving sensors”

• Describe forest complexity using metrics derived from the data processed using 3D rendering algorithms

Estimating Forest Biomass and Stand Structure via Drone Mounted Lidar and RGB/NIR Call name:
DB0160 2019 - 2021

Role in this project: Project coordinator

Coordinating institution: Oregon State University

Project partners: Oregon State University ()

Affiliation: Oregon State University ()

Project website:

Abstract:

Fuels mitigation projects have become a significant emphasis for NRCS funds in northeastern Oregon, address changes in forest density and species composition resulting from a century of fire exclusion. The effectiveness of these projects depends both upon reduction of forest fuels and upon changes in horizontal and vertical distribution of those fuels. To date, project accomplishments have been documented as number of acres treated, but that measurement does little to characterize actual reductions in biomass or changes in forest structure.

This project will access the feasibility of using drone-mounted Lidar and/or visible (RGB) and near infrared (NIR) optical sensors to estimate changes in biomass and structure. It will be structured as follows:

Local NRCS, Oregon Department of Forestry, and Wallow County Soil and Water Conservation partners will work with OSU to identify sites scheduled for fuels-reduction treatments this summer and/or fall. Sites will be selected to support a replicated factorial, randomized design, capturing the range of slopes, forest types, and type of sensors mounted on the UAV. A minimum of 3 replicates, possible 5, will be included. Each replicate, consists of one plot between 5 – 10 acres. The levels of each factor are resented in the following summary of the experiment:

Reliable achievement of Douglas-fir stand management objectives using real time precision forestry Call name:
2019-67019-29462 2019 - 2021

Role in this project: Project coordinator

Coordinating institution: Oregon State University

Project partners: Oregon State University ()

Affiliation: Oregon State University ()

Project website:

Abstract:

The goal of the project is to develop a novel management technology that links activities scheduled to achieve desired silvicultural outcomes with the science base that helps define parameters of those activities. From the analytical perspective, the project’s goal consists of simultaneous optimization of the decisions for an area with the decisions for a specific operating neighborhood, approximately 100 m2. We argue that the disconnect between theory and practice mainly come from the lack of applicable sensors and data processing pipelines that can reliably gather real-time silvicultural data from a harvesting operator. Those data can then inform real-time decision-making that simultaneously optimizes the stand – level harvesting decisions with the decisions for a specific location that the operator is concerned. Hence, this project proposes an embodied approach that encompasses real-time data collection from sensors mounted on the harvesting equipment, data analysis, and multi-objective optimization of harvesting decisions, which can then inform harvesting operators in real-time and lead to better harvesting practices that inch closer to theoretical optimality.

Lower John Day UAV Protocol development Project Call name:
2019-DEQ391-OSU 2018 - 2020

Role in this project: Project coordinator

Coordinating institution: Oregon State University

Project partners: Oregon State University ()

Affiliation: Oregon State University ()

Project website:

Abstract:

This pilot project is located in the Ferry Canyon/John Day River Watershed (See appendix A). The watershed is approximately 81,000 acres and is part of the Lower John Day sub-basin (See Appendix B). Ferry Canyon features steep John Day Canyons surrounded by grasslands, sage steppe, CRP at the ridge tops, and dry land agriculture on the Columbia Plateau (See Appendix C). Ferry Canyon Watershed is home to 10.5 miles of priority native spawning habitat as a tributary of the John Day River (See Appendix D).

Tree level forest planning using remote sensing data Call name: Joint Applied Research Projects - PCCA-2011 call, Type 2
PN-II-PT-PCCA-2011-3.2-1710 2012 - 2016

Role in this project: Project coordinator

Coordinating institution: INSTITUTUL NAŢIONAL DE CERCETARE-DEZVOLTARE ÎN SILVICULTURĂ "MARIN DRĂCEA"

Project partners: INSTITUTUL NAŢIONAL DE CERCETARE-DEZVOLTARE ÎN SILVICULTURĂ "MARIN DRĂCEA" (RO); INSTITUTUL NATIONAL DE CERCETARE DEZVOLTARE PENTRU STIINTE BIOLOGICE (RO); UNIVERSITATEA "ŞTEFAN CEL MARE" DIN SUCEAVA (RO); OCOLUL SILVIC CODRII BEIUSULUI RA (RO)

Affiliation: INSTITUTUL NATIONAL DE CERCETARE DEZVOLTARE PENTRU STIINTE BIOLOGICE (RO)

Project website: http://www.roifn.ro

Abstract:

The strategic forest planning process aims at optimization of a series of objectives that are subject to a set of constraints. The incorporation of market dynamics in the optimization increases the complexity of the strategic planning formulation by requiring the inclusion of detailed tree level products (such as sawlogs, pulpwood, or chip ‘n saw) in the planning process. Furthermore, constant adjustment of planning objectives to short-term market changes requires fast and accurate identification of the products that can be supplied by the forest estate subject to strategic planning. The advent of remote sensing techniques, especially LIDAR, reduces the time to acquire accurate information required for products allocation. The present research aims at identification of products allocation based on LIDAR information that would supply the optimal solution to the strategic planning objectives. LIDAR data will be used to determine a series of tree attributes (e.g., total height, crown width, crown length and crown asymmetry) that will be used to delineate possible products to be obtained from a tree. Models describing each tree in terms of products allocation will be developed by adjusting taper equations to the attributes estimated using LIDAR. The optimal products allocation would be determined at the forest estate level using several planning algorithms, including linear programming, simulated annealing, and first fit decreasing algorithm constrained to fulfill the perfect bin-packing theorem. The research will provide the information needed by Romanian forest operators to adjust their strategic and tactical planning to market conditions.

Assessment of forest planning sensitivity to product allocation using remote sensing data Call name:
2012 - 2016

Role in this project: Project coordinator

Coordinating institution: Louisiana Tech University

Project partners: Louisiana Tech University ()

Affiliation: Louisiana Tech University ()

Project website:

Abstract:

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects